{"title":"单击“在线广告垃圾邮件预防模型”","authors":"N. Zingirian, M. Benini","doi":"10.5121/ijnsa.2018.10101","DOIUrl":null,"url":null,"abstract":"This paper shows a vulnerability of the pay-per-click accounting of Google Ads and proposes a statistical tradeoff-based approach to manage this vulnerability. The result of this paper is a model to calculate the overhead cost per click necessary to protect the subscribers and a simple algorithm to implement this protection. Simulations validate the correctness of the model and the economical applicability.","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Click Spam Prevention Model for Online Advertisement\",\"authors\":\"N. Zingirian, M. Benini\",\"doi\":\"10.5121/ijnsa.2018.10101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper shows a vulnerability of the pay-per-click accounting of Google Ads and proposes a statistical tradeoff-based approach to manage this vulnerability. The result of this paper is a model to calculate the overhead cost per click necessary to protect the subscribers and a simple algorithm to implement this protection. Simulations validate the correctness of the model and the economical applicability.\",\"PeriodicalId\":241211,\"journal\":{\"name\":\"CompSciRN: Artificial Intelligence (Topic)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-01-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CompSciRN: Artificial Intelligence (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/ijnsa.2018.10101\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CompSciRN: Artificial Intelligence (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/ijnsa.2018.10101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Click Spam Prevention Model for Online Advertisement
This paper shows a vulnerability of the pay-per-click accounting of Google Ads and proposes a statistical tradeoff-based approach to manage this vulnerability. The result of this paper is a model to calculate the overhead cost per click necessary to protect the subscribers and a simple algorithm to implement this protection. Simulations validate the correctness of the model and the economical applicability.